Description

The decision to implement environmental protection options is a political one. These, and other political and social decisions affect the balance of the ecosystem and how the point of equilibrium desired is to be reached. This book develops a stochastic, temporal model of how political processes influence and are influenced by ecosystem processes and looks at how to find the most politically feasible plan for managing an at-risk ecosystem. Finding such a plan is accomplished by first fitting a mechanistic political and ecological model to a data set composed of observations on both political actions that impact an ecosystem and variables that describe the ecosystem. The parameters of this fitted model are perturbed just enough to cause human behaviour to change so that desired ecosystem states occur. This perturbed model gives the ecosystem management plan needed to reach desired ecosystem states. To construct such a set of interacting models, topics from political science, ecology, probability, and statistics are developed and explored. Key features: * Explores politically feasible ways to manage at-risk ecosystems.
* Gives agent-based models of how social groups affect ecosystems through time. * Demonstrates how to fit models of population dynamics to mixtures of wildlife data. * Presents statistical methods for fitting models of group behaviour to political action data. * Supported by an accompanying website featuring datasets and JAVA code. This book will be useful to managers and analysts working in organizations charged with finding practical ways to sustain biodiversity or the physical environment. Furthermore this book also provides a political roadmap to help lawmakers and administrators improve institutional environmental management decision making.

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About Author

Timothy C. Haas, Lubar School of Business Administration, University of Wisconsin at Milwaukee. Timothy Haas is involved in teaching undergraduate and graduate courses in statistical methods, pursuing decision making and environmental statistics research, and collaborating with faculty on application of statistics to Marketing and Economics.